Thermal Control Algorithms for Predictive Patient Temperature Management
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Solution Overview
Problem
Thermal control systems for patient temperature management lack the ability to adapt and improve over time, failing to optimize settings and predictions based on user data and sensor inputs effectively.
Innovation Solution
Integration of machine learning capabilities into thermal control units to select user-preferred settings and predict events such as patient shivering or temperature overshoot, utilizing data from various sensors, including those not initially used, to enhance algorithm performance and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning capabilities are integrated into thermal control units, then the system's ability to adapt and optimize settings improves, but device complexity increases
Solution Approach 1:
A machine learning module serves as an intermediary between sensor inputs and control decisions, processing data and generating optimized settings without requiring complex manual programming. The module receives sensor data, applies trained algorithms, and outputs control parameters, effectively mediating between raw data and actionable decisions.
Solution Approach 2:
The thermal control system performs self-optimization through machine learning algorithms that automatically adjust settings based on historical data and sensor inputs. The system learns from past operations and autonomously improves its performance without external intervention, reducing the need for manual calibration and complex configuration.
2Measurement precision
If machine learning algorithms use additional sensor data, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
Existing sensors in the thermal control system are made multi-functional by utilizing their data for both traditional control functions and machine learning predictions. The same temperature and flow sensors provide inputs for both immediate thermal regulation and predictive analytics, maximizing the value of existing components without adding dedicated prediction sensors.
Solution Approach 2:
The machine learning model is trained in advance using historical sensor data and clinical outcomes, so that when deployed, it can make accurate predictions using routine sensor measurements. This preliminary training phase separates the complexity of model development from operational complexity, allowing the system to achieve high prediction accuracy using standard sensors during actual use.
3Ease of operation
If automatic setting selection is implemented, then ease of operation improves, but loss of information increases
Solution Approach 1:
The machine learning system continuously monitors sensor data and compares predicted outcomes with actual results, using this feedback to refine settings and improve future predictions. This closed-loop feedback ensures that automatic setting selection maintains accuracy while reducing operational complexity, as the system learns from discrepancies and adjusts accordingly.
Solution Approach 2:
The system dynamically adjusts the level of automation based on confidence levels and operational context. When prediction confidence is high or the situation is routine, automatic settings are applied. When uncertainty is high or unusual conditions are detected, the system can transition to manual mode or request additional information, maintaining ease of operation while preserving necessary information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the operational efficiency and accuracy of thermal control systems by automatically selecting optimal settings and predicting events, leading to better patient temperature management and enhanced user experience.
Implementation Method 1
The heat exchanger is adapted to add or remove heat from the fluid circulating in the circulation channel
Implementation Method 2
The pump circulates fluid through the circulation channel from the fluid inlet to the fluid outlet
Implementation Method 3
The fluid temperature sensor is adapted to sense a temperature of the fluid
Data Source
AI summary
A thermal control system for controlling a patient's temperature includes a thermal control unit and an off-board computing device. The thermal control unit includes a fluid inlet, a fluid outlet, a pump, a heat exchanger, a display, one or more sensors, a transceiver, and a controller. The thermal control system employs one or more machine learning techniques to perform one or more of the following: automatically implement one or more user-preferred settings, automatically predict the occurrence of one or more events based on analyses of prior events, and/or automatically improve one or more algorithms based on analyses of additional sensor data. The machine learning techniques may be implemented onboard the thermal control unit and/or may be implemented at a remote computing device (e.g. a server) that collates and analyzes data from multiple thermal control units, and then sends the results of the analyses back to the thermal control units.